"""Module for generating physical equations using machine learning."""
from transformers import GPT2LMHeadModel, GPT2Tokenizer


class EquationGenerator:
    """A class to generate physical equations using machine learning algorithms."""

    def __init__(self, data, model_name="gpt2"):
        """
        Initialize the EquationGenerator with a machine learning model and training data.

        Args:
            model: A machine learning model for generating physical equations.
            data: Preprocessed training data.
        """
        self.tokenizer = GPT2Tokenizer.from_pretrained(
            model_name
        )  # Load the tokenizer for the model
        self.model = GPT2LMHeadModel.from_pretrained(
            model_name
        )  # Load the model itself
        self.data = data  # Store the training data for later use

    def train(self, epochs, batch_size):
        """
        Train the machine learning model using the provided data.

        Args:
            epochs: Number of epochs to train the model.
            batch_size: Batch size for training.
        """
        # Note: GPT2 models from transformers are pre-trained
        # Fine-tuning requires additional setup with training datasets
        # This is a placeholder for the fine-tuning logic
        print(f"Training with {epochs} epochs and batch size {batch_size}")
        print("Note: Fine-tuning GPT2 requires additional setup")


    def generate_equation(self, input_text, max_length=50, num_return_sequences=1):
        """
        Generate a physical equation using the trained machine learning model.

        Args:
            input_text: Input text for generating the equation.
            max_length: Maximum length of the generated sequence.
            num_return_sequences: Number of sequences to generate.

        Returns:
            generated_equations: A list of generated equation strings.
        """
        input_ids = self.tokenizer.encode(input_text, return_tensors="pt")

        # Generate output sequences
        output_sequences = self.model.generate(
            input_ids=input_ids,
            max_length=max_length,
            num_return_sequences=num_return_sequences,
            no_repeat_ngram_size=2,
            temperature=0.7,
            top_k=50,
            top_p=0.95,
            do_sample=True,
        )

        # Decode and return the generated sequences
        generated_equations = []
        for sequence in output_sequences:
            decoded_sequence = self.tokenizer.decode(
                sequence, skip_special_tokens=True
            )
            generated_equations.append(decoded_sequence)

        return generated_equations

    def save_model(self, file_path):
        """
        Save the trained machine learning model to a file.

        Args:
            file_path: Path to save the model.
        """
        self.model.save_pretrained(file_path)
        self.tokenizer.save_pretrained(file_path)
